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Digital Twin-Driven Machine Condition Monitoring: A Literature Review

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Digital Twin-Driven Machine Condition Monitoring : A Literature Review. / Liu, He; Xia, Min; Williams, Darren et al.

In: Journal of Sensors, Vol. 2022, 6129995, 30.07.2022.

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Liu H, Xia M, Williams D, Sun J, Yan H, Xiao X. Digital Twin-Driven Machine Condition Monitoring: A Literature Review. Journal of Sensors. 2022 Jul 30;2022:6129995. doi: 10.1155/2022/6129995

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Bibtex

@article{07e5c63d52694ca1913f435bc2079004,
title = "Digital Twin-Driven Machine Condition Monitoring: A Literature Review",
abstract = "Digital twin (DT), aiming to characterise behaviors of physical entities by leveraging the virtual replica in real time, is an emerging technology and paradigm at the forefront of the Industry 4.0 revolution. The implementation of DT in predictive maintenance has facilitated its growth. As a major component of predictive maintenance, condition monitoring (CM) has great potential to combine with DT. To describe the state-of-the-art of DT-driven CM, this paper delivers a systematic review on the theoretical and practical development of DT in advancing CM. The evolution of concepts, main research areas, applied domains, and related key technologies are summarised. The driver of DT for CM is detailed in three aspects: data support, capability enhancement, and maintenance mode shift. The implementation process of DT-driven CM is introduced from the classification of DT modelling and the extension of monitoring algorithms. Finally, current challenges and opportunities for future research are discussed especially concerning the barriers and gaps in data management, high-fidelity modelling, behavior characterisation, framework standardisation, and uncertainty quantification.",
keywords = "Review Article",
author = "He Liu and Min Xia and Darren Williams and Jianzhong Sun and Hongsheng Yan and Xueliang Xiao",
year = "2022",
month = jul,
day = "30",
doi = "10.1155/2022/6129995",
language = "English",
volume = "2022",
journal = "Journal of Sensors",
issn = "1687-725X",
publisher = "Hindawi Limited",

}

RIS

TY - JOUR

T1 - Digital Twin-Driven Machine Condition Monitoring

T2 - A Literature Review

AU - Liu, He

AU - Xia, Min

AU - Williams, Darren

AU - Sun, Jianzhong

AU - Yan, Hongsheng

AU - Xiao, Xueliang

PY - 2022/7/30

Y1 - 2022/7/30

N2 - Digital twin (DT), aiming to characterise behaviors of physical entities by leveraging the virtual replica in real time, is an emerging technology and paradigm at the forefront of the Industry 4.0 revolution. The implementation of DT in predictive maintenance has facilitated its growth. As a major component of predictive maintenance, condition monitoring (CM) has great potential to combine with DT. To describe the state-of-the-art of DT-driven CM, this paper delivers a systematic review on the theoretical and practical development of DT in advancing CM. The evolution of concepts, main research areas, applied domains, and related key technologies are summarised. The driver of DT for CM is detailed in three aspects: data support, capability enhancement, and maintenance mode shift. The implementation process of DT-driven CM is introduced from the classification of DT modelling and the extension of monitoring algorithms. Finally, current challenges and opportunities for future research are discussed especially concerning the barriers and gaps in data management, high-fidelity modelling, behavior characterisation, framework standardisation, and uncertainty quantification.

AB - Digital twin (DT), aiming to characterise behaviors of physical entities by leveraging the virtual replica in real time, is an emerging technology and paradigm at the forefront of the Industry 4.0 revolution. The implementation of DT in predictive maintenance has facilitated its growth. As a major component of predictive maintenance, condition monitoring (CM) has great potential to combine with DT. To describe the state-of-the-art of DT-driven CM, this paper delivers a systematic review on the theoretical and practical development of DT in advancing CM. The evolution of concepts, main research areas, applied domains, and related key technologies are summarised. The driver of DT for CM is detailed in three aspects: data support, capability enhancement, and maintenance mode shift. The implementation process of DT-driven CM is introduced from the classification of DT modelling and the extension of monitoring algorithms. Finally, current challenges and opportunities for future research are discussed especially concerning the barriers and gaps in data management, high-fidelity modelling, behavior characterisation, framework standardisation, and uncertainty quantification.

KW - Review Article

U2 - 10.1155/2022/6129995

DO - 10.1155/2022/6129995

M3 - Journal article

VL - 2022

JO - Journal of Sensors

JF - Journal of Sensors

SN - 1687-725X

M1 - 6129995

ER -